the RATP Project on metro access control

Object Categorization Based on a Video and Optical Cell System

Participants: Francois Bremond, Binh Bui,Monique Thonnat.

We have presented a real-time system for shape recognition. This system is a video and multi-sensor platform with a fixed camera observing the mobile objects from the top and lateral sensors observing the side of mobile objects. This system is able to classify the mobile objects evolving in the scene into several expected categories. The key of the recognition method is to compute mobile object properties (i.e. width, height, density of occluded/non-occluded sensors) by making use of the top camera and lateral sensors and then to apply Bayesian classifiers to these properties. A learning phase based on ground truth data is used to train the Bayesian classifiers. Our recognition method has been integrated into an existing access control device used in public transportation (subway) at RATP to improve safety and comfort, to prevent fraud and to count people for statistical matters. The expected categories in this case are mainly "adult", "child", and "baggage" (i.e. suitcases, bag, and backpack).

Figure 1. A platform combining video and a multi-sensor system (leds, optical cells).

We have also studied a degraded operating mode of the system, i.e., we do not use the top camera for categorizing mobile objects. We have also tested other classification methods with our actual database. Our goal is, on one hand, to compare the performance of both systems (with and without the top camera) and, on the other hand, to make a comparison between our classification method and other methods such as those based on support vector machines (SVM) and neural networks (NN).

Figure 2. Recognition of "adult with child (see mpg 626K) "
Figure 3. Recognition of "two overlapping adults (see mpg 462K) "